Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12864-015-2140-x
Title: GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data
Authors: Harmston, N 
Ing-Simmons, E
Perry, M
Bareši, A
Lenhard, B.
Keywords: chromatin
chromosome
genome
animal
chromatin
computer graphics
genetic database
genetics
genomics
human
K-562 cell line
metabolism
molecular genetics
mouse
procedures
software
thymocyte
chromatin
Animals
Chromatin
Computer Graphics
Databases, Genetic
Genomics
Humans
K562 Cells
Mice
Molecular Sequence Annotation
Software
Thymocytes
Issue Date: 2015
Publisher: BioMed Central Ltd.
Citation: Harmston, N, Ing-Simmons, E, Perry, M, Bareši, A, Lenhard, B. (2015). GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data. BMC Genomics 16 (1) : 963. ScholarBank@NUS Repository. https://doi.org/10.1186/s12864-015-2140-x
Abstract: Background: Precise quantitative and spatiotemporal control of gene expression is necessary to ensure proper cellular differentiation and the maintenance of homeostasis. The relationship between gene expression and the spatial organisation of chromatin is highly complex, interdependent and not completely understood. The development of experimental techniques to interrogate both the higher-order structure of chromatin and the interactions between regulatory elements has recently lead to important insights on how gene expression is controlled. The ability to gain these and future insights is critically dependent on computational tools for the analysis and visualisation of data produced by these techniques. Results and conclusion: We have developed GenomicInteractions, a freely available R/Bioconductor package designed for processing, analysis and visualisation of data generated from various types of chromosome conformation capture experiments. The package allows the easy annotation and summarisation of large genome-wide datasets at both the level of individual interactions and sets of genomic features, and provides several different methods for interrogating and visualising this type of data. We demonstrate this package's utility by showing example analyses performed on interaction datasets generated using Hi-C and ChIA-PET. © 2015 Harmston et al.
Source Title: BMC Genomics
URI: https://scholarbank.nus.edu.sg/handle/10635/174274
ISSN: 14712164
DOI: 10.1186/s12864-015-2140-x
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